These 2d examples are both from Winsong Chang's excellent R Graphics Cookbook
library(corrplot)
corrplot(mcor)
# for the dataset
library(ggplot2)
rm(mtcars)
mcor <- cor(mtcars)
# Print mcor and round to 2 digits
round(mcor, digits = 2)
## mpg cyl disp hp drat wt qsec vs am gear carb
## mpg 1.00 -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.60 0.48 -0.55
## cyl -0.85 1.00 0.90 0.83 -0.70 0.78 -0.59 -0.81 -0.52 -0.49 0.53
## disp -0.85 0.90 1.00 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39
## hp -0.78 0.83 0.79 1.00 -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75
## drat 0.68 -0.70 -0.71 -0.45 1.00 -0.71 0.09 0.44 0.71 0.70 -0.09
## wt -0.87 0.78 0.89 0.66 -0.71 1.00 -0.17 -0.55 -0.69 -0.58 0.43
## qsec 0.42 -0.59 -0.43 -0.71 0.09 -0.17 1.00 0.74 -0.23 -0.21 -0.66
## vs 0.66 -0.81 -0.71 -0.72 0.44 -0.55 0.74 1.00 0.17 0.21 -0.57
## am 0.60 -0.52 -0.59 -0.24 0.71 -0.69 -0.23 0.17 1.00 0.79 0.06
## gear 0.48 -0.49 -0.56 -0.13 0.70 -0.58 -0.21 0.21 0.79 1.00 0.27
## carb -0.55 0.53 0.39 0.75 -0.09 0.43 -0.66 -0.57 0.06 0.27 1.00
library(xtable)
print(xtable(mcor), type = "html")
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| mpg | 1.00 | -0.85 | -0.85 | -0.78 | 0.68 | -0.87 | 0.42 | 0.66 | 0.60 | 0.48 | -0.55 |
| cyl | -0.85 | 1.00 | 0.90 | 0.83 | -0.70 | 0.78 | -0.59 | -0.81 | -0.52 | -0.49 | 0.53 |
| disp | -0.85 | 0.90 | 1.00 | 0.79 | -0.71 | 0.89 | -0.43 | -0.71 | -0.59 | -0.56 | 0.39 |
| hp | -0.78 | 0.83 | 0.79 | 1.00 | -0.45 | 0.66 | -0.71 | -0.72 | -0.24 | -0.13 | 0.75 |
| drat | 0.68 | -0.70 | -0.71 | -0.45 | 1.00 | -0.71 | 0.09 | 0.44 | 0.71 | 0.70 | -0.09 |
| wt | -0.87 | 0.78 | 0.89 | 0.66 | -0.71 | 1.00 | -0.17 | -0.55 | -0.69 | -0.58 | 0.43 |
| qsec | 0.42 | -0.59 | -0.43 | -0.71 | 0.09 | -0.17 | 1.00 | 0.74 | -0.23 | -0.21 | -0.66 |
| vs | 0.66 | -0.81 | -0.71 | -0.72 | 0.44 | -0.55 | 0.74 | 1.00 | 0.17 | 0.21 | -0.57 |
| am | 0.60 | -0.52 | -0.59 | -0.24 | 0.71 | -0.69 | -0.23 | 0.17 | 1.00 | 0.79 | 0.06 |
| gear | 0.48 | -0.49 | -0.56 | -0.13 | 0.70 | -0.58 | -0.21 | 0.21 | 0.79 | 1.00 | 0.27 |
| carb | -0.55 | 0.53 | 0.39 | 0.75 | -0.09 | 0.43 | -0.66 | -0.57 | 0.06 | 0.27 | 1.00 |
library(igraph)
# Specify edges for a directed graph
gd <- graph(c(1, 2, 2, 3, 2, 4, 1, 4, 5, 5, 3, 6))
plot(gd)
# For an undirected graph
gu <- graph(c(1, 2, 2, 3, 2, 4, 1, 4, 5, 5, 3, 6), directed = FALSE)
# No labels
plot(gu, vertex.label = NA)
This example is from Yihui's response to a stack overflow question.
Need to load the CanvasMatrix library and the hook_webgl code to get 3d graphs to work
knit_hooks$set(webgl = hook_webgl)
<script src="https://dl.dropbox.com/u/15335397/misc/CanvasMatrix.js"></script>
library(rgl)
x <- sort(rnorm(1000))
y <- rnorm(1000)
z <- rnorm(1000) + atan2(x, y)
plot3d(x, y, z, col = rainbow(1000))
You must enable Javascript to view this page properly.
open3d()
## [1] 2
spheres3d(x, y, z, col = rainbow(1000))
You must enable Javascript to view this page properly.
Author: Jim Hester Created: 2013 Mar 27 10:54:38 PM Last Modified: 2013 Mar 28 02:28:35 PM
from statmethods.net
# ggplot2 examples
library(ggplot2)
# rm any local mtcars
rm(mtcars)
## Warning: object 'mtcars' not found
# use color brewer as default discrete colors
scale_colour_discrete <- function(...) scale_color_brewer(palette = "Set1",
...)
scale_fill_discrete <- function(...) scale_fill_brewer(palette = "Set1", ...)
# create factors with value labels
mtcars$gear <- factor(mtcars$gear, levels = c(3, 4, 5), labels = c("3gears",
"4gears", "5gears"))
mtcars$am <- factor(mtcars$am, levels = c(0, 1), labels = c("Automatic", "Manual"))
mtcars$cyl <- factor(mtcars$cyl, levels = c(4, 6, 8), labels = c("4cyl", "6cyl",
"8cyl"))
grouped by number of gears (indicated by color)
qplot(mpg, data = mtcars, geom = "density", fill = gear, alpha = I(0.5), main = "Distribution of Gas Milage",
xlab = "Miles Per Gallon", ylab = "Density")
for each combination of gears and cylinders in each facet, transmission type is represented by shape and color
qplot(hp, mpg, data = mtcars, shape = am, color = am, facets = gear ~ cyl, size = I(3),
xlab = "Horsepower", ylab = "Miles per Gallon")
Seperate for each number of cylinders
qplot(wt, mpg, data = mtcars, geom = c("point", "smooth"), method = "lm", formula = y ~
x, color = cyl, main = "Regression of MPG on Weight", xlab = "Weight", ylab = "Miles per Gallon")
observations (points) are overlayed and jittered
qplot(gear, mpg, data = mtcars, geom = c("boxplot", "jitter"), fill = gear,
main = "Mileage by Gear Number", xlab = "", ylab = "Miles per Gallon")
Author: Jim Hester Created: 2013 Mar 20 10:57:07 AM Last Modified: 2013 Mar 20 03:30:06 PM
library(ggplot2)
crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
library(reshape2) # for melt
crimesm <- melt(crimes, id = 1)
require(maps)
states_map <- map_data("state")
ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat)
last_plot() + coord_map()
ggplot(crimesm, aes(map_id = state)) + geom_map(aes(fill = value), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat) + facet_wrap(~variable)
These are the same plots with fig.show='hold' in the options
ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat)
last_plot() + coord_map()
ggplot(crimesm, aes(map_id = state)) + geom_map(aes(fill = value), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat) + facet_wrap(~variable)
Author: Jim Hester Created: 2013 Mar 28 02:44:48 PM Last Modified: 2013 Mar 28 03:18:18 PM
Author: Jim Hester Created: 2013 Mar 28 03:22:28 PM Last Modified: 2013 Mar 28 03:23:17 PM